🚀 WE GOT OUR OWN SERVER! FixIt 2.0 Relaunch + Lifetime Pricing Hey everyone! Big news - after months of running on expensive cloud infrastructure, we finally pulled the trigger on our own hardware. Last week, we installed a Dell PowerEdge R740xd in our rack. This isn't just a hardware upgrade. This changed EVERYTHING about how we can run FixIt. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💰 THE ECONOMICS THAT FORCED THIS DECISION Cloud Costs (What We Were Paying): - Compute: $400/month - Storage: $150/month - Bandwidth: $80/month - TOTAL: $630/month = $7,560/year Our Server: - Power: ~$50/month - Internet: Already paying - TOTAL: $50/month = $600/year Break-even: 24 months Savings after year 3: $7,000+/year But the real win isn't just cost. It's CONTROL. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚡ THE PERFORMANCE TRANSFORMATION Before (Cloud Infrastructure): ❌ 300-500ms average response time ❌ 4.7M solutions (storage limits) ❌ 95% uptime (shared resources) ❌ Throttling during peak usage ❌ Can't optimize hardware ❌ Expensive to scale After (Our Server): ✅ 29-100ms average response time (10x faster!) ✅ 18.5M solutions (4x larger database) ✅ 99.9% uptime guarantee ✅ Zero throttling, ever ✅ Custom optimizations for our workload ✅ Predictable, fixed costs ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🖥️ THE HARDWARE SPECS We didn't cheap out. This is enterprise-grade: Dell PowerEdge R740xd: - CPU: Dual Intel Xeon Silver 4216 (32 cores, 2.10GHz) - RAM: 314GB DDR4 ECC - Storage: 13TB RAID6 array - GPU: Dual NVIDIA Tesla T4 (30GB VRAM) - Network: Dual 10GbE - Power: Redundant power supplies - Uptime: 99.9% SLA Database: - Size: 82.9GB (was 20GB on cloud) - Records: 18,563,455 Stack Overflow solutions - Engine: Custom-optimized SQLite with FTS5 - Index: Multi-column full-text search ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💎 NEW PRICING (BECAUSE WE OWN THE HARDWARE) Here's what's amazing: because we control costs now, we can offer pricing that actually makes sense for developers.

Exciting progress on a massive medical literature embedding project! Currently running a distributed GPU pipeline that's churning through the entire PubMed corpus - that's 4.48 million biomedical research articles. 📊 Current Stats: • 655,500 articles processed (~15% complete) • Dual RTX 3090 setup maintaining perfect 50/50 load balance • Processing rate: 41,000+ articles per hour • Zero thermal throttling over 18 hours continuous operation 🔬 Technical Highlights: • Custom load balancing achieving exactly 454,721 chunks per GPU • Chunk-based processing handling variable article lengths (avg 1.35 chunks/article) • Automated checkpointing every 100 batches for fault tolerance • Real-time monitoring with temperature-aware throttling ⏱️ Timeline: Started: Friday afternoon Expected completion: Thursday morning Total compute time: ~109 hours Building comprehensive medical AI embeddings requires serious compute dedication. This dataset will enable semantic search across decades of medical research - from clinical trials to drug discovery papers. The beauty of well-engineered pipelines? They run through the weekend while you sleep